Most histories of
complexity are heroic adventures, focusing entirely on advance; ours is
critical, exploring the challenges holding back the study of social complexity.
The complexity sciences are not the disruptive, transdisciplinary science they
promised to be.
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Map, Special Collections, University of Amsterdam
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CHAPTER 2 SUMMARY:
Chapter 2
is all about setting the context for the tour the Atlas takes. You must set
sail from somewhere. Our point of departure is the challenging and problematic place
the complexity sciences find themselves in, presently, in terms of the study of
social complexity.
Critically
mapping that problematic space is the focus of Chapter 3. As outlined in that
chapter, we have identified a set of thirteen situations that are holding the study of social
complexity back from being a truly transdisciplinary disruptive science.
Chapter 2
focuses on understanding the historical pathways by which the study of social
complexity ended up in a challenging space in the first place. We start by defining
complexity. From there we present a critical history of the study of social
complexity, focusing on four major historical shifts:
(1)
the emergence of the complexity sciences.
(2)
the invasion of natural scientists into everything social.
(3)
the 1990s complexity turn in the social sciences.
(4)
the social science turn in complexity, circa 2020s, which is the shift
that holds our attention for the rest of the book.
Map of the early years of the complexity sciences, with a
focus on the Santa Fe Institute
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Johanna Bergmann, John Casti, 2002
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Map of the complexity sciences, circa 2022. The field has
massively expanded, but also, in the process, become watered down, evolving
into ‘normal’ science in the Kuhnian sense of the word.
No, Not Everything is Scale-Free!!!!
Of the various examples we provide in Chapter 2 of how, historically speaking, the study of social complexity has evolved into the current predicament it faces, one of the most straightforward is the study of scale-free networks.
There is significant controversy around whether scale-free networks exist, given that the definition requires that the distribution fit a power-law, which most networks with long-tails do not meet. The problem is that, in the real-world, scale-free networks
that fit a power law are not so easily identified and mapped. After the initial
excitement resided, then, in the 1990s,researchers started running into all sorts of problems
fitting the tails of these distributions.What they found was that, while lots of
scale-free networks have the classic heavy tail distribution, they do not
regularly fit the power law criteria.
Here is the problem: instead of evolving the concept in response to the empirical data, divisions emerged, with many of those involved in its initial conception holding on to the concept as sacred. In fact, these are literally the words used by Voitalov and
colleagues’ in their 2019 article, 'Scale-Free Networks Well Done’, which
is a defence of scale-free networks. They
state, “Scale-free and power-law are sacral words in network science, a
mature field that studies complex systems in nature and society by representing
these systems as networks of interacting elements” (p. 1).
(For more on this controversy,
see Erica
Klarreich’s article, CLICK
HERE.
For Barabasi’s response, amongst, others, CLICK
HERE.)
What is our point? We are not saying complexity science cannot be without controversies.
We are saying the opposite. It should be controversial! For us, most of science
is a dialogue, a critical argument, in search of useful insights into how the
world works. When concepts in the complexity sciences are considered sacred, one
is venturing into troublesome territory, with boundaries being built around
ideas, including who is allowed to say what and when, as in the case of natural
science over social science. If the complexity sciences are to be continually transdisciplinary
and disruptive for the purposes of studying social complexity, the field needs
a bit of a shake. It needs what we are calling a social science turn – a critical
reflection on complexity from a different perspective, it needs a rigorous and
critical engagement with the social sciences it sought to advance.
REFERENCES
1.
Anna D. Broido and Aaron
Clauset, ‘Scale-Free Networks Are Rare’, Nature Communications 10, no. 1
(December 2019): 1017.
2.
Aaron Clauset, Cosma Rohilla Shalizi, and M. E. J. Newman, ‘Power-Law
Distributions in Empirical Data’, SIAM Review 51, no. 4 (4 November
2009): 661–703.
3.
Brian Castellani and Rajeev
Rajaram, ‘Past the Power Law: Complex Systems and the Limiting Law of
Restricted Diversity’, Complexity 21, no. S2 (2016): 99–112.
4.
Voitalov et al., ‘Scale-Free
Networks Well Done’, Physical Review Research 1, no. 3 (18 October
2019): 033034, https://doi.org/10.1103/PhysRevResearch.1.033034.
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